Morphological Separation in Bill Dimensions Across Penguin Species

2D density contours with individual observations for the three species

TidyTuesday
Data Visualization
R Programming
2025
Exploring morphological differences between Adelie, Chinstrap, and Gentoo penguin species through bill dimensions analysis. This visualization demonstrates how bill length and depth measurements create distinct clusters that allow for clear species differentiation in the Palmer Archipelago penguin dataset.
Author

Steven Ponce

Published

April 16, 2025

Figure 1: A scatterplot showing bill dimensions (length vs. depth in mm) for three penguin species with density contours. The plot clearly displays three distinct clusters: Adelie penguins (orange, n=152) with shorter, deeper bills centered around 40mm length; Chinstrap penguins (blue, n=68) with medium-length bills around 49mm and greater depth; and Gentoo penguins (green, n=124) with longer, shallower bills around 48mm length and 15mm depth. Diamond markers indicate species means.

Steps to Create this Graphic

1. Load Packages & Setup

Show code
## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
    tidyverse,      # Easily Install and Load the 'Tidyverse'
    ggtext,         # Improved Text Rendering Support for 'ggplot2'
    showtext,       # Using Fonts More Easily in R Graphs
    janitor,        # Simple Tools for Examining and Cleaning Dirty Data
    skimr,          # Compact and Flexible Summaries of Data
    scales,         # Scale Functions for Visualization
    glue,           # Interpreted String Literals
    here,           # A Simpler Way to Find Your Files
    camcorder       # Record Your Plot History
    )
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  =  8,
    height =  8,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))

2. Read in the Data

Show code
penguins <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-04-15/penguins.csv') |>
    clean_names()

3. Examine the Data

Show code
glimpse(penguins)
skim(penguins)

4. Tidy Data

Show code
### |-  tidy data ----
species_stats <- penguins |>
    group_by(species) |>
    summarize(
        mean_bill_len = mean(bill_len, na.rm = TRUE),
        mean_bill_dep = mean(bill_dep, na.rm = TRUE),
        n = n(),
        .groups = 'drop'
    )
# label positions
label_positions <- data.frame(
    species = c("Adelie", "Chinstrap", "Gentoo"),
    x_pos = c(38.5, 47, 48),  
    y_pos = c(20.3, 19.5, 14.5),  
    n = c(152, 68, 124)
)

5. Visualization Parameters

Show code
### |-  plot aesthetics ----
colors <- get_theme_colors(
    palette = c(
        "Adelie" = "#FF8C00",      
        "Chinstrap" = "#4169E1",   
        "Gentoo" = "#2E8B57"      
    )
)

### |-  titles and caption ----
title_text <- str_wrap("Morphological Separation in Bill Dimensions Across Penguin Species",
                       width = 45)
subtitle_text <- str_glue("2D density contours with individual observations for the three species")

# Create caption
caption_text <- create_social_caption(
    tt_year = 2025,
    tt_week = 15,
    source_text =  "{ basepenguins } R package" 
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
    base_theme,
    theme(
        # Text styling 
        plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.14), margin = margin(b = 10)),
        plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)),
        
        # Axis elements
        axis.title = element_text(color = colors$text, face = "bold", size = rel(0.8)),
        axis.text = element_text(color = colors$text, size = rel(0.7)),
        
        # Grid elements
        panel.grid.minor = element_line(color = "gray50", linewidth = 0.05),
        panel.grid.major = element_line(color = "gray50", linewidth = 0.02),
        
        # Legend elements
        legend.position = "plot",
        legend.direction = "horizontal",
        legend.title = element_text(family = fonts$text, size = rel(0.8), face = "bold"),
        legend.text = element_text(family = fonts$text, size = rel(0.7)),
        
        # two-row legend
        legend.box.spacing = unit(0.4, "cm"),
        legend.key.width = unit(1.5, "cm"),
        legend.spacing.x = unit(0.2, "cm"),
 
        legend.box = "horizontal",
        legend.box.just = "left",
        
        # Plot margins 
        plot.margin = margin(t = 20, r = 20, b = 20, l = 20),
    )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code
### |-  Plot  ----
p <- ggplot(penguins, aes(x = bill_len, y = bill_dep, color = species)) +
    # Geoms
    geom_point(
        alpha = 0.12, size = 1.5
        ) +
    geom_density_2d(
        size = 0.8, 
        alpha = 0.9,
        bins = 6,
        lineend = "round",
        linejoin = "round"
    ) +
    geom_point(
        data = species_stats,
        aes(x = mean_bill_len, y = mean_bill_dep),
        size = 4,
        shape = 18
    ) +
    geom_label(
        data = label_positions,
        aes(x = x_pos, y = y_pos, 
            label = paste0(species, "\nn = ", n),
            color = species),
        fill = "white",
        alpha = 0.9,
        fontface = "bold",
        label.size = 0.2,  
        label.padding = unit(0.4, "lines"),
        label.r = unit(0.15, "lines")  
    ) +
    # Scales
    scale_color_manual(values = colors$palette) +
    scale_x_continuous(limits = c(30, 62)) +
    scale_y_continuous(limits = c(13, 21.5)) +
    # Labs
    labs(
        title = title_text,
        subtitle = subtitle_text,
        caption = caption_text,
        x = "Bill Length (mm)",
        y = "Bill Depth (mm)"
        ) +
    # Theme
    theme(
        plot.title = element_text(
            size = rel(1.85),
            family = fonts$title,
            face = "bold",
            color = colors$title,
            lineheight = 1.1,
            margin = margin(t = 5, b = 5)
        ),
        plot.subtitle = element_text(
            size = rel(0.95),
            family = fonts$subtitle,
            color = alpha(colors$subtitle, 0.9),
            lineheight = 1.2,
            margin = margin(t = 5, b = 10)
        ),
        plot.caption = element_markdown(
            size = rel(0.65),
            family = fonts$caption,
            color = colors$caption,
            hjust = 0.5,
            margin = margin(t = 10)
        )
    )

7. Save

Show code
### |-  plot image ----  
save_plot(
  plot = p, 
  type = "tidytuesday", 
  year = 2025, 
  week = 15, 
  width = 8,
  height = 8
)

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] camcorder_0.1.0 here_1.0.1      glue_1.8.0      scales_1.3.0   
 [5] skimr_2.1.5     janitor_2.2.0   showtext_0.9-7  showtextdb_3.0 
 [9] sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0  
[13] stringr_1.5.1   dplyr_1.1.4     purrr_1.0.2     readr_2.1.5    
[17] tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0
[21] pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.49          htmlwidgets_1.6.4  tzdb_0.4.0        
 [5] yulab.utils_0.1.8  vctrs_0.6.5        tools_4.4.0        generics_0.1.3    
 [9] curl_6.0.0         parallel_4.4.0     gifski_1.32.0-1    fansi_1.0.6       
[13] pkgconfig_2.0.3    ggplotify_0.1.2    lifecycle_1.0.4    farver_2.1.2      
[17] compiler_4.4.0     munsell_0.5.1      repr_1.1.7         codetools_0.2-20  
[21] snakecase_0.11.1   htmltools_0.5.8.1  yaml_2.3.10        crayon_1.5.3      
[25] pillar_1.9.0       MASS_7.3-60.2      magick_2.8.5       commonmark_1.9.2  
[29] tidyselect_1.2.1   digest_0.6.37      stringi_1.8.4      labeling_0.4.3    
[33] rsvg_2.6.1         rprojroot_2.0.4    fastmap_1.2.0      grid_4.4.0        
[37] colorspace_2.1-1   cli_3.6.3          magrittr_2.0.3     base64enc_0.1-3   
[41] utf8_1.2.4         withr_3.0.2        bit64_4.5.2        timechange_0.3.0  
[45] rmarkdown_2.29     bit_4.5.0          hms_1.1.3          evaluate_1.0.1    
[49] knitr_1.49         markdown_1.13      gridGraphics_0.5-1 rlang_1.1.4       
[53] isoband_0.2.7      gridtext_0.1.5     Rcpp_1.0.13-1      xml2_1.3.6        
[57] renv_1.0.3         svglite_2.1.3      rstudioapi_0.17.1  vroom_1.6.5       
[61] jsonlite_1.8.9     R6_2.5.1           fs_1.6.5           systemfonts_1.1.0

9. GitHub Repository

The complete code for this analysis is available in tt_2025_15.qmd.

For the full repository, click here.

10. References

  1. Data Sources:

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